Multi-label feature selection algorithm with imbalance label otherness

Abstract

In view of the fact that most of the existing feature selection algorithms do not consider the possible differences existing in the sample description by different labels, a multi-label feature selection algorithm with imbalance label otherness (MSIO) is proposed. The frequency distributions of positive and negative labels under different labels are added to the process of feature selection as the label weight, the traditional method of calculating information entropy is modified to get a more efficient feature sequence. Based on ML-kNN (multi-label k-nearest neighbor), the features are classified on 11 multi-label benchmark datasets of Mulan database, and the algorithms of multi-label dimensionality reduction via dependency maximization (MDDM), pairwise multivariate mutual information (PMU), feature selection for multi-label naive Bayes classification (MLNB), multi-label feature selection with label correlation (MUCO) and MSIO algorithm are evaluated. Experimental results and statistical hypothesis tests show that MSIO algorithm has good stability, high classification accuracy, and certain effectiveness and superiority.